AI use cases: the first pilot has to be mature, measurable, and accountable

With AI use cases, the most exciting idea usually gets the attention. But for the first pilot, something else counts: is the candidate already clear enough today, measurable, and accountable in operation?

That's often where the first mistake starts: people look for ideas for AI instead of processes that can already be sensibly improved with AI today. Then come the typical AI wish lists: a chatbot for all company knowledge, automatic meeting minutes, an agent that ideally takes over sales while it's at it.

Some of that can become useful. But as a first use case it's often too broad, too hard to measure, or too far from actual work processes.

A viable AI use case doesn't begin with the question of where AI could theoretically be deployed. It begins with the choice of process.

Does it occur often enough? Can the sequence be clearly described? Is the necessary data available and clean? Can the result be checked? Is there an economic lever that justifies the effort? And is there someone who actually owns the case in operation?

If several of these answers stay open, the idea isn't automatically bad. It's just probably not a good first pilot.

Then comes a second mistake that happens often: an AI topic can be strategically very relevant and still come too early.

For instance because the process can't yet be stably defined, the data isn't available or accessible in sufficient quality, or ownership in the department isn't settled.

That's exactly where many AI pilots hit a dead end: not because of bad ideas, but because good ideas are treated as pilot-ready too early.

The better way is more sober: observe processes, collect candidates, filter strictly. Only then decide which pilot is really mature.

I've described this logic in more detail here: https://sixtyfour.solutions/praxis/ki-use-cases-identifizieren/